Tag: menu

Analytics for Menu Presentation

Last week, I presented a single-column format for menu selling on an iPhone, with the glib recommendation to let analytics determine the sort order.  Today, I will expand on that.  Our task is to sort the list of products in descending order of their relevance to the current deal, which includes vehicle data, consumer preferences, and financing terms.

This sorting task is the same whether we are flipping through web pages or scrolling down the mobile display.  The framework I present here is generalized and abstract, making the task better suited to automation, but ignoring the specific F&I knowledge we all take for granted.  I’ll come back to that later.

For now, let’s assume we have six products to present, called “Product One,” and so on, and four questions that will drive the sorting.  Assume these are the usual questions, like, “how long do you plan on keeping the car?”

That answer will be in months or years, and the next one might be in miles, but we are going to place them all on a common scale from zero to one (I warned you this would be abstract).  Think of using a slider control for each input, where the labels can be anything but the range is always 0.0 to 1.0.

Next, assign four weights to each product, representing how relevant each question is for that product.  The weights do not have to be zero to one, but I recommend keeping them all around the same starting magnitude, say 1 to 5.  Weights can also be negative.

For example, if there’s a question about loan-to-value, that’s important for GAP.  High LTV will correlate positively with GAP sales.  If you word that question the other way, the correlation will still be strong, but negative.  So, now you have a decision matrix that looks something like this:

Yes, we are doing weighted factor analysis.  Let’s say that, for a given deal, the answers to our four questions are, in order:

[0.3, 0.7, 0.1, 1.0]

To rank the products for this deal we simply multiply the decision matrix by the deal vector.  I have previously confessed my weak vector math skills, but I am certain that Python has an elegant way to do this:

Product Two ranks first, because of its affinity for high-scoring Question Four.  Product Four takes second place, thanks to the customer’s response to Question Two – whatever that may be.  By now, you may have noticed that this is the setup for machine learning.

If you are blessed with “big data,” you can use it to train this system.  In a machine learning context, you may have hundreds of data points.  In addition to deal data and interview questions, you can use clickstream data, DMS data, contact history, driving patterns (?) and social media.

If not, you will have to use your F&I savvy to set the weights, and then adjust them every thousand deals by manually running the numbers.

For example, we ask “how long will you keep the car?” because we know when the OEM warranty expires.  Given make, model, and ten thousand training deals, an AI will dope out this relationship on its own.  We will do it by setting one year past the warranty as 0.1, two as 0.2, etc.  We can also set a variable indicating how complete the manufacturer’s coverage is.

Same story with GAP.  Give the machine a loan amount and a selling price, and it will “discover” the correlation with GAP sales.  If setting the weights manually, set one for LTV and then calculate the ratio for each deal.

Lease-end protection, obviously, we only want to present on a lease deal.  But we don’t want it to crowd out, say, wearables.  So, weight it appropriately on the other factors, but give it big negative weights for cash and finance deals.

I hope this gives some clarity to the analytics approach.  In a consumer context, there is no F&I manager to carefully craft a presentation, so some kind of automation is required.

Menu Selling on an iPhone

Followers of my Twitter feed know that I have lately been looking at mobile apps, to see if anyone can present protection products on an iPhone.  I wrote about this three years ago and, according to my informal survey, the field is still open.

I don’t think anybody has a good way to present a menu on a consumer web site, much less an iPhone.

Not only is the iPhone a restrictive form factor but we must assume that the customer, not an F&I person, is operating it.  We would like to apply our Best Practices for Menu Selling, but the app must be able to apply them on its own.

For example, if we want to retain the package concept with the carefully chosen payment intervals, we can use an accordion control.  I proposed this for a client once, in an F&I context, but it doesn’t make sense for consumer use.

No, the best way to “present all the products, all the time,” is simply to make one long column with everything in it.  The iPhone presents challenges, but there are offsetting advantages.  We can show fifteen products in one column, and the customer has his leisure to scroll through them.

I prefer scrolling to swiping for a few reasons.  In the prototype shown here, we have the obligatory vehicle photo.  After the first scroll, that’s gone and the screen space is devoted to products.

The prototype shows monthly prices for the vehicle and the products.  This assumes the finance process is settled, and the app can choose products matching the finance term.  Touching any of the products will open up a full page with details, coverage choices, and a “sales tool” as in the earlier article.

I recommend using analytics to determine the sequence of products in the column, and even to A/B test the format of the product blurbs.  I have in mind a few different formats:

  • Text with graphic and price, as shown here.
  • No price ‘til you open it.
  • Lead with the sales tool.

I discuss analytics here, but I am not a fan of the full “ownership survey.”  Of the eight standard questions, maybe you can sneak in one or two elsewhere in the process.  Apart from that, we’re counting on data points found in the deal itself.

I also think “less is more” when confronting the customer with choices.  As you can see in the mockup above, there must be no complicated grades of coverage (or deductible).  If you’re configuring the app for a specific dealer, you may want to filter some options out of the dealer’s product table.

Depending on who’s managing the app, the products themselves may be rethought.  If you want to offer chemical, dent, key, and windshield as a combo product, then that’s a single choice.  Alternatively – since we have unlimited  column space – you can offer each one individually.  What you do not want is a product having fifteen different combinations.

Coming back to my informal survey of mobile apps, and the workflow given here, I believe there are already good examples of vehicle selection, credit application, trade valuation, and payment calculator.  Menu selling has been the only missing link, until now.

Best Practices for Menu Selling

I was asked recently to opine on this topic, which I do today with some reservation, for I can see the venerable four-column menu approaching its sell-by date.  The image shown here is a MenuVantage prototype from 2003.  Don’t get me wrong.  As I wrote here, this is still the best tool for the traditional setting in the F&I office … for as long as that setting prevails.

Best practices for menu selling split into two broad categories: those that are good for selling, and those that are good for compliance.  I will present them in that order.

Every product appropriate to the transaction type and “car status” of the current deal (i.e. Used Lease) should appear in column one.  Some menu systems use deal templates, making it easy to select the proper layout every time.

The home court advantage in the F&I suite is that you can do a four-column menu, and there is a professional there to present it.

For most systems, column one automatically drives the layout of the accept/decline “waiver” form.  This is best practice for compliance, and it’s good selling too.  Why have a product that you only present on special occasions?

The practical limit for products in column one is six, maybe eight, so choose wisely when laying out the menu template.  Using bundles will allow you to squeeze in more products.  I generally don’t like bundled products, as I wrote here, but this is a reason to use them.

Every menu should include a second, longer term, with the correct APR for that term.  There is a charming story about this in Six Month Term Bump, plus a downloadable spreadsheet.  Twelve months is overkill, and likely to raise an objection.

The amount of product you can finance without changing the monthly payment is given by this formula.  Without doing the annuity math, a good approximation is: base payment times five.The monthly payment in column four should be roughly $30 more than the base payment without products.  That way, you draw the customer’s attention into the menu without a big price barrier.  Likewise, payments should increase in small increments from right to left across the bottom of the menu.

Obviously, the increments will be larger for more expensive deals, say 10% of the base payment.  This is easy to do, if you are manually setting up each menu.  It takes a little more planning to do this with templates.  You can either tweak the individual products at deal time, or you can set up a different template for highline vehicles.

For example, offer the platinum VSC coverage in column one and the gold in column two.  By the way, do not reuse the VSC coverage choices (like gold, silver, and platinum) as your column headings.  That’s an obvious source of confusion.  Finally, your menu system should feature sales tools and custom content for each product, like the famous depreciation chart for GAP.

I have a few more recommendations, related to compliance.  If you already have a good grasp of unfair and deceptive practices, you can skip this part.  Be warned, though, that consumer watchdogs and regulatory agencies are looking over your shoulder.

The chart below (and the pull quote) is from the National Consumer Law Center.  You can tell that the dealer in green is using a menu system with a fixed markup over dealer cost.  The dealer in red is certainly making more PVR but he is also courting a federal discrimination charge.

Menu trainers like to say, “present all the products to all the customers, all the time.”  They might add, “at the same price.”  The NCLC report goes on to show that minority car buyers are systemically charged more for the same products.  Some dealers simply don’t allow the F&I manager to vary from the calculated retail price.  In states like Florida, that’s the law.

Giving F&I managers the discretion to charge different consumers different prices for the same product … is a recipe for abuse.

The menu should display the price of each product, not just the package price.  Some turn this into a selling feature by also showing the price as a daily amount.  It makes a good layout to have the most expensive product at the top, with prices descending down the column.

All of these measures require some kind of audit trail.  I have seen some very strong systems that track exactly what was presented, by whom, when, for how much, and whether the price was changed.  At a minimum, you should collect the customer’s signature on the waiver form, with all the products, their prices, and your standard disclosure text.

Next week, I will resume writing about the brave new world of flow selling, self-closing, and predictive analytics.  We may find that many of these practices – especially regarding compliance – are still relevant.

Wanted: eCommerce Product Manager

Gartner Group says “the API is the product.”  I am looking for an experienced product manager who knows what Gartner Group is and why they say that.  The API in question is Safe-Guard’s collection of dealer-facing web services.  This is a topic I have worked on and written about extensively, as here, and now I plan to try the product manager approach.

The successful candidate will have solid product management experience, preferably with an API, and maybe some pragmatic marketing or agile development.  Software development experience a plus.  Self-starter.  Relocation.   Salary commensurate with experience.

Predictive Selling in F&I

We have all seen how Amazon uses predictive selling, and now this approach is finding its way into our industry.  In this article I compare and contrast different implementations, and discuss how the technique may be better suited to online than to the F&I suite.

If you read Tom Clancy, you might like Lee Childs.  If you bought a circular saw, you might need safety goggles.  To draw these inferences, Amazon scans for products that frequently occur together in the order histories of its customers.  You can imagine that given their volume of business, Amazon can fine-tune the results by timeframe, department, price, and so on.

The effectiveness of predictive selling depends on two things: the strength of your algorithms, and the depth of your database.  Automotive Mastermind claims to use “thousands of data points,” mined from the DMS, social media, and credit bureaus.  An online auto retailer or platform site (see my taxonomy here) will also have data about which web pages the customer viewed.  Your typical F&I menu is lucky if it can read data from the DMS.

The face of predictive selling in F&I is the automated interview.  We all know the standard questions:

  • How long do you plan on keeping the car?
  • How far do you drive to work?
  • Do you park the car in a garage?
  • Do you drive on a gravel road?
  • Do you transport children or pets?

A system that emulates the behavior of an expert interviewer is called, appropriately, an “expert system.”  I alluded to expert systems for F&I here, in 2015, having proposed one for a client around the same time.  This is where we can begin to make some distinctions.

Rather than a set of canned questions, a proper expert system includes a “rules editor” wherein the administrator can add new questions, and an “inference engine” that collates the results.  Of course, the best questions are those you can answer from deal data, and not have to impose on the customer.

A data scientist may mine the data for buying patterns, an approach known as “analytics,” or she may have a system to mine the data automatically, an approach known as “machine learning.”  You know you have good analytics when the system turns up an original and unanticipated buying pattern.  Maybe, for example, customers are more or less likely to buy appearance protection based on the color of their vehicle.

At the most basic level, predictive selling is about statistical inference.  Let’s say your data mining tells you that, of customers planning to keep the car more than five years, 75% have bought a service contract.  You may infer that the next such customer is 75% likely to follow suit, which makes the service contract a better pitch than some other product with a 60% track record.  One statistic per product hardly rises to the level of “analytics,” but it’s better than nothing.

Another thing to look at is the size of the database.  If our 75% rule for service contract is based on hundreds of deals, it’s probably pretty accurate.  If it’s based on thousands of deals, that’s better.  Our humble data scientist won’t see many used, leased, beige minivans unless she has “big data.”  Here is where a dealer group that can pool data across many stores, or an online selling site, has an advantage.

If you are implementing such a system, you not only have a challenge getting enough data, you also have to worry about contaminating the data you’ve got.  You see, pace Werner Heisenberg, using the data also changes the data.  Customers don’t arrive in F&I already familiar with the products, according to research from IHS.

Consider our service contract example.  Your statistics tell you to present it only for customers keeping their vehicle more than five years.  That now becomes a self-fulfilling prophecy.  Going forward, your database will fill up with service contract customers who meet that criterion because you never show it to anyone else.

You can never know when a customer is going to buy some random product.  This is why F&I trainers tell you to “present every product to every customer, every time.”  There is a technical fix, which is to segregate your sample data (also known as “training data” for machine learning) from your result data.  The system must flag deals where prediction was used to restrict the presentation, and never use these deals for statistics.

Doesn’t that mean you’ll run out of raw data?  It might, if you don’t have a rich supply.  One way to maintain fresh training data is periodically to abandon prediction, show all products, let the F&I manager do his job, and then put that deal into the pool of training data.

Customers complete a thinly disguised “survey” while they’re waiting on F&I, which their software uses to discern which products to offer and which ones the customer is most likely to buy based upon their responses.

Regulatory compliance is another reason F&I trainers tell you to present every product every time.  Try telling the CFPB that “my statistics told me not to present GAP on this deal.”  There’s not a technical fix for that.

One motivation for the interview approach, versus a four-column menu, is that it’s better suited to form factors like mobile and chat.  This is a strong inducement for the online selling sites.  In the F&I suite, however, the arguments are not as strong.  Trainers are uniformly against the idea that you can simply hand over the iPad and let it do the job for you.

No, I have not gone over to the Luddites.  This article offers advice to people developing (or evaluating) predictive selling systems, and most of the advantages accrue to the online people.  The “home court advantage” in the F&I suite is that you can do a four-column menu, and there is a professional there to present it.

Wanted: Experienced F&I Trainer

I am in the process of creating an eCommerce department for Safe-Guard.  Regular readers know that I specialize in creating new organizations, and my record is pretty good.  The training function, which is also a kind of sales function, is likely to grow.  So, this is an opportunity to get in early.

The job is to train all of the F&I managers who sell products administered by Safe-Guard, and ensure they know how to present them properly using any of the top ten menu systems.  For one person, at least to begin with, this will be a challenge.  We are in thousands of dealerships.

Thus, the successful candidate must have the skill and temperament to leverage the resources of our affiliated agents, vendors, manufacturers, and dealer groups.  Self-starter.  Travel.  Proficiency in F&I procedures and software, notably menu systems.  Salary commensurate with experience.

Six Month Term Bump for Menu Selling

Whenever I design a menu system, I always include a second finance term that defaults to the base term plus six months. When I did the first menu system for AutoNation, I was coached on this by Arthur Knosala who learned it, I believe, at JM&A.

We had an object lesson when I was working on Route One’s menu. The team was just getting into this requirement when the product owner happened to buy a new car, and took the term bump. She was able to maintain the agreed payment, and still buy some good products.  Even a three-month bump is significant.

My spreadsheet, below, shows how this works. The idea is to goal-seek the amount of product that maintains the original monthly payment, at the longer term. The input values are blue. Everything else is calculated. This allows the possibility that the APR may be higher with the longer term.

Term Bump

If your menu system won’t do this, you can download my spreadsheet. It automatically calculates the finance amount which, with the term bump, results in the same payment. Remember, only type in the blue cells.

Magic tricks are easy once you know the secret — Marshall Brodien

When I was at MenuVantage, one of the guys put together a demo in which he used the term bump to sell a raft of products, and then a biweekly payment program to ratchet the term back down.  It was like a magic trick.  Same payment, same term, and presto!  He pulls two thousand dollars’  gross out of his sleeve.  Dealers couldn’t sign up fast enough.